One common objective of communication technology is to transmit information using a minimum number of bits, without losing important intelligence, by removing the redundancies in the original information. In the wireline/wireless speech communication field, advancements in speech compression have resulted in compression ratios of 1:10 or better. This compression is typically implemented using speech codecs (encoder and decoder) that use signal transformations. However, these transformations also increase the processing complexity required to encode and decode voice signals. This complexity can add a significant cost to enhancements providing higher channel density on an existing backbone. Hence, in practice, there is a trade-off between the computation complexity (based on the compression technique) and degradation in speech quality.
The Code-Excited-Linear-Prediction (CELP) is one of the techniques used in speech codecs that currently offers an optimal performance in the quality-complexity space. Several alternate realizations of CELP have been brought forward such as Algebraic CELP (ACELP), Qualcomm CELP (QCELP), Relaxed CELP (RCELP), and others, with varying degrees of complexity. Currently, the ACELP realization is widely used, since it avoids the larger memory requirements of CELP. ACELP aims at searching the best codebook excitation vector by minimizing the Mean Square Error (MSE) or maximizing the correlation between the weighted speech signal and the weighted synthesized speech signal.
In typical ACELP codec standards such as ITU-T G.729A/B, GSM-EFR, GSM-AMR, TIA/EIA-EVRC the maximum complexity lies in a single place—the random excitation codebook search, which may be up to one third of a codec encoder operational capacity. Accordingly, reduction of the complexity of a codebook search can significantly increase the capacity of a codec without adding cost.